Nurse telephone education for promoting a treat‐to‐target approach in recently diagnosed rheumatoid arthritis patients: A pilot project
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to implement a nurse telephone education programme for patients with recently diagnosed rheumatoid arthritis (RA) that promotes shared decision-making and a treat-to-target approach. METHODS: This was a pilot project of newly diagnosed adult RA patients conducted between November 2015 and December 2016. A rheumatology clinic nurse telephoned patients to offer disease education. A toolkit was mailed to patients. Measures included call attempts, call time, a qualitative description of free-text notes and the proportion of patients who adhered to their next clinic visit. Data were analysed descriptively and qualitatively. RESULTS: Twenty-six patients participated in the nurse calls. Most patients were female (65%), with a median age of 54 years (range 22-78 years). Median call length was 14.5 min, with a range of 8-23 min. Qualitative notes indicated that patients overwhelmingly supported the nurse calls. Nineteen patients (73%) were adherent to their follow-up visit. CONCLUSION: This preliminary project successfully implemented an educational programme that included a nurse-facilitated, RA-specific, telephone call and toolkit. This educational programme could be a model for similar educational efforts by other clinics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".